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Incompleteness of Atomic Structure Representations

Publications associées (32)

Efficient and insightful descriptors for representing molecular and material space

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

EPFL2024

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Ksenia Briling

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

EPFL2024

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Justin Villard

Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...

EPFL2023

The role of Zn in C-S-H atomic structure, nucleation and growth

Anna Morales Melgares

The most promising solution towards cementitious materials with a lower carbon footprint is the partial substitution of the clinker by supplementary cementitious materials (SCMs) such as fly ash, blast furnace slag, limestone and calcined clays. The produc ...

EPFL2023

Gas/Surface reactions probed by reflection absorption infrared spectroscopy

Harmina Vejayan

In my thesis, I present an investigation of the dissociation reactions of gas phase molecules on single crystal metal surfaces studied by a molecular beam in combination with Reflection Absorption Infrared Spectroscopy (RAIRS). Two gas/surface systems were ...

EPFL2023

Machine-learning the electronic density of states: electronic properties without quantum mechanics

Chiheb Ben Mahmoud

The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture and is central to modern electronic structure theory. It also underpins the computation and interpretation ...

EPFL2023

Robustness of Local Predictions in Atomistic Machine Learning Models

Michele Ceriotti, Chiheb Ben Mahmoud, Federico Grasselli, Sanggyu Chong

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

Amer Chemical Soc2023

Universal machine learning for the response of atomistic systems to external fields

Yaolong Zhang

Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in curren ...

Nature Portfolio2023

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Kevin Rossi

Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...

AMER PHYSICAL SOC2023

koopmans: An Open-Source Package for Accurately and Efficiently Predicting Spectral Properties with Koopmans Functionals

Nicola Marzari, Giovanni Borghi, Ngoc Linh Nguyen, Nicola Colonna, Riccardo De Gennaro, Edward Baxter Linscott, Andrea Ferretti

Over the past decade we have developed Koopmans functionals, a computationally efficient approach for predicting spectral properties with an orbital-density-dependent functional framework. These functionals impose a generalized piecewise linearity conditio ...

Amer Chemical Soc2023

Electromagnetic processes of nuclear excitation: from direct photoabsorption to free electron and muon capture

Simone Gargiulo

In the vast expanse of the Universe and on our planet, nuclei exist in a state of excitement. These excited nuclear states (isomers) can persist for varying periods, from fractions of a second to billions of years and beyond, before decaying to their groun ...

EPFL2023

Atomic scale volume and grain boundary diffusion elucidated by in situ STEM

Johann Michler, Amit Sharma, Laszlo Pethö, Xavier Maeder

Diffusion is one of the most important phenomena studied in science ranging from physics to biology and, in abstract form, even in social sciences. In the field of materials science, diffusion in crystalline solids is of particular interest as it plays a p ...

Nature Portfolio2023

Beyond potentials: Integrated machine learning models for materials

Michele Ceriotti

Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit ...

SPRINGER HEIDELBERG2022

Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties

Michele Ceriotti, Michael John Willatt, Jigyasa Nigam

Symmetry considerations are at the core of the major frameworks used to provide an effective mathematical representation of atomic configurations that is then used in machine-learning models to predict the properties associated with each structure. In most ...

AIP Publishing2022

Unified theory of atom-centered representations and message-passing machine-learning schemes

Michele Ceriotti, Jigyasa Nigam, Guillaume André Jean Fraux, Sergey Pozdnyakov

Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-cen ...

AIP Publishing2022

Machine learning for metallurgy V: A neural-network potential for zirconium

William Curtin, Pandula Manura Liyanage

The mechanical performance-including deformation, fracture and radiation damage-of zirconium is determined at the atomic scale. With Zr and its alloys extensively used in the nuclear industry, understanding that atomic scale behavior is crucial. The defect ...

2022

Nuclear Excitation by Free Muon Capture

Fabrizio Carbone, Ivan Madan, Simone Gargiulo

Efficient excitation of nuclei via exchange of a real or virtual photon has a fundamental importance for nuclear science and technology development. Here, we present a mechanism of nuclear excitation based on the capture of a free muon into the atomic orbi ...

AMER PHYSICAL SOC2022

Predicting hot-electron free energies from ground-state data

Michele Ceriotti, Chiheb Ben Mahmoud, Federico Grasselli

Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally excited electrons, that i ...

AMER PHYSICAL SOC2022

Roadmap on Machine learning in electronic structure

Michele Ceriotti, Andrea Grisafi, Max David Veit, Jigyasa Nigam

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by ...

IOP Publishing Ltd2022

Cavity Quantum Electrodynamics with strongly correlated fermions

Kevin Etienne Robert Roux

This thesis presents the first cavity quantum electrodynamics experiments performed with a degenerate gas of

^6

Li with strong atom-atom interactions. The first part of this manuscript describes the design and the building of the apparatus that has been e ...

EPFL2022

Publications associées (32)

^6

Li with strong atom-atom interactions. The first part of this manuscript describes the design and the building of the apparatus that has been e ...

EPFL2022